Lead Generation Approaches in Drug Discovery (Rankovic/Lead Generation) || High Throughput Screening Approach to Lead Discovery

2 High Throughput ScreeningApproach to Lead Discovery

ZORAN RANKOVIC*, CRAIG JAMIESON, ANDRICHARD MORPHY

Medicinal Chemistry, Schering-Plough Corporation, Newhouse,Lanarkshire, ML1 5SH, UK. [email protected]

CONTENTS

2.1 Introduction 22

2.2 Historical Background 22

2.2.1 Early Years: Focus on Numbers and Speed 22

2.2.2 Phase 2: In Pursuit of Quality 23

2.3 Screening Collection Enhancement Strategies 30

2.3.1 Screening Libraries 31

2.3.2 Compound Acquisition 41

2.3.3 Size Matters 43

2.4 Screening Strategies 45

2.4.1 Mixtures versus Single Compound Screening 45

2.4.2 Full Deck Screening 45

2.4.3 Focused Screening 46

2.4.4 Sequential Screening 46

2.5 HTS Assays and Equipment 47

2.6 Hit Confirmation and Assessment 50

2.6.1 Hit Confirmation Workflow 50

2.6.2 Post Screen Heuristics 50

2.6.3 Retesting at Single Concentration 51

2.6.4 Expert Analysis 52

2.6.5 Hit Confirmation 54

2.6.6 Hit Profiling 56

2.7 HTS: Successes, Failures, and Examples 58

2.8 Summary and Outlook 60

References 62

Lead Generation Approaches in Drug Discovery,Edited by Zoran Rankovic and Richard Morphy.Copyright r 2010 John Wiley & Sons, Inc.

21

2.1. INTRODUCTION

For most of the last century, drugs were discovered starting from endogenousligands, natural products, or marketed drugs followed by chemical modifica-tions to improve efficacy in in vivo models [1]. With the breakthroughs inbiochemistry in the 1970s and advances in molecular biology in the 1980s, thissuccessful approach was supplemented by in vitro assays with increasedthroughput. The ambition was to screen an ever-increasing number ofcompounds to identify leads for numerous new targets derived from the humangenome project. In the early 1990s, the inception of high throughput screening(HTS), and the advent of combinatorial chemistry heralded a new age of drugdiscovery. The ability to screen hundreds of thousands, if not millions, ofcompounds in a high throughput fashion to populate company pipelines is nowwidely recognized as a central paradigm in modern drug discovery. Morerecently, HTS has been recognized by the academic community as providing anavenue for identifying chemical probes to investigate biological systems and theeffects of target modulation [2].

2.2. HISTORICAL BACKGROUND

Since its inception in the early 1990s, HTS has undergone a rapid and profoundevolution. Two distinct phases could be delineated from this period. The earlyyears were characterized by a brute force approach with the main emphasis onsheer numbers of compounds and speed of their synthesis and screening.Lessons learned from this period had suggested ‘‘less is more’’ and triggered asecond phase characterized by more emphasis on quality.

2.2.1. Early Years: Focus on Numbers and Speed

The emergence of a new drug discovery paradigm in the early 1990s powered bycombinatorial chemistry, genomics, and HTS technologies created unprece-dented optimism within the pharmaceutical industry and wider public [3].There were high expectations that this approach would deliver multiple newstarting points for each target derived from the human genome project, andnew drugs with improved efficacy and safety profile would quickly follow [4].Screening throughput and the size of compound collections across the industryrapidly increased as the high expectations led to considerable investment in thefurther development of automation, assay technologies, and combinatorialchemistry.

Before the HTS revolution, most pharmaceutical companies possessedcollections of only a few thousand compounds originating mainly fromhistorical projects. The advent of HTS in the early 1990s triggered theexpansion of corporate compound collections at a staggering rate. By themid-1990s the number of compounds available for screening in major

22 HIGH THROUGHPUT SCREENING APPROACH TO LEAD DISCOVERY

pharmaceutical companies was already around 50,000 and by end of the decadewas over 500,000. The majority of this expansion was driven by combinatorialchemistry and purchasing compounds from vendors. Initially, vendors weremainly providing compounds acquired from academic labs, often in the formerSoviet Union, in variable quantities and qualities. However, against thebackground of ever-increasing demand, this business model rapidly evolvedinto what is currently a multimillion dollar industry, offering well over 10million compounds of generally high purity [5].

However, as it often happens with new technologies, the early optimism wassoon replaced by deep skepticism. By the late 1990s it became apparent thatdespite unprecedented investment, the tangible deliverables from HTS fell wellshort of expectations, raising concerns about the effectiveness of this newapproach [6]. Screening campaigns provided very few tractable leads, positivelyimpacting only a small proportion of discovery projects [7]. Poor hit rates andhigh attrition at the discovery stage were attributed primarily to the suboptimalquality of corporate screening files brought by a numbers-driven compoundacquisition strategy that focused on rapid expansion and paid too littleattention to quality.

2.2.2. Phase 2: In Pursuit of Quality

Once the importance of the quality of corporate screening collection becameapparent by the end of the 1990s, the process of weeding out undesirablematerial began. The focus was initially on the purity and identity of compoundsin the collection. In the next stage, the emphasis shifted toward removal ofundesirable structural features using chemical filters (structural alerts), andthen advanced further to consider physicochemical properties that influence thein vivo profile of a compound.

2.2.2.1. Sample Purity and Identity The purity of samples entering screeningcollections had been compromised in the early 1990s in order to meet thedemands of highly ambitious expansion programs that were designed tosupport the increasing HTS throughput. The basic premise of the ‘‘en-masse’’approach was that a massive size expansion of the screening pool was likely toenhance the chance of finding hits. So much so that even if only a smallproportion of all potential hits in the file were to be identified, it would still besufficient to initiate successful drug discovery programs for every target ofinterest. Hence, the purity and overall quality of compounds in screeningcollections were considered to be less important compared to the actual numberof compounds in the collection.

By the mid-1990s, having endured several cycles of resource-intensive yetlargely fruitless pursuits for new HTS-derived leads, most of the industry hadshifted the focus from the synthesis and testing libraries of mixtures to that ofdiscrete compounds. Critically, the emphasis was still on the number ofcompounds at the expense of their purity. Since the output of high throughput

2.2. HISTORICAL BACKGROUND 23

synthesis was far beyond the library purification and analysis capabilities of thetime, only a tiny proportion of library compounds were purified and fullycharacterized. Consequently, screening collections continued to be populatedby uncharacterized, often crude reaction mixtures, despite being originallyintended as discrete compounds. It transpired that the screening of suchlibraries regularly produced high false positive hit rates and had attendantissues with hit confirmation, resulting in a substantial waste of time andresources [8]. It has also been demonstrated that the degradation rate duringthe storage of compounds with lower purity is greater compared to those withhigher purity [9]. All of these findings provided a strong justification forinvestments into the development and acquisition of high throughput purifica-tion systems [10]. Use of resin-bound capturing reagents and automated liquid–liquid extractors (LLE) were amongst the earliest investigated library purifica-tion techniques, both of which unfortunately proved to be of limited scope andonly partially successful in removing excess reagents. Similarly to LLEs,automated solid-phase extraction (SPE) was more suitable for relatively smallhit and lead optimization libraries containing up to several hundred com-pounds, rather than diverse screening libraries of over a thousand compounds.

Particular attention has been given to high performance liquid chromato-graphy (HPLC) methods due to their wide applicability and adaptability toautomation. Semipreparative systems can purify 50–100 mg of crude materialper injection using standard reverse-phase binary acetonitrile/water gradientsover a relatively short period of time. Traditional HPLC systems used mainlyfor repetitive purifications of sequential batches were adapted for high through-put by incorporation of automated sample injection and fraction collectiondevices. Importantly, replacement of the traditional UV peak detection by real-time mass spectrometry to trigger fraction collection (liquid chromatographymass spectrometry, LC-MS) eliminated the need for collection of large fractionarrays, and consequently significantly improved the throughput and logistics oflibrary purification.

After raising the quality-based entry criteria and providing the relevantinfrastructure for new compound production, attention inevitably shiftedtoward the quality of compounds already in the screening collection. Indeed,understanding the quality of the screening collection has been seen as the firststep toward its improvement. In a pursuit of the ‘‘pure and sure’’ ideal, someresearch organizations undertook the enormous task of analysing their entirescreening collections. In the case of the combined heritage collections ofSmithKline Beecham and GlaxoWellcome at the time of their merger, thenew collection contained well over a million compounds [11]. Analysis of thewhole was achieved using custom-built HPLC systems equipped with a384-well-plate autosampler and a sequence of diode array (DAD), evaporativelight scattering (ELSD), and mass spectrometry (positive- and negative-ionelectrospray) detectors. A fast gradient method with a 5-min cycle time persample allowed analysis of 50,000 compounds per month. The analysis showedjust over 60 percent of compounds met the internal quality requirements.


Interestingly, the pass rate was very similar for the two heritage collectionsdespite their quite different composition and storage conditions, suggestingthat the observed quality level may well be typical for historical collections inother big pharmaceutical companies.

The dominance of LC-MS as the method of choice for library purificationand analysis has been seriously challenged over the recent years by advancesmade in supercritical fluid chromatography (SFC). This technique is a variationof normal phase HPLC in which a compressed supercritical gas (carbon dioxidenear its critical temperature of 311C and pressure of 73 bar) is used as a mobilephase with methanol as the polar component of the binary gradient. Thisconfers several advantages, including the possibility of using higher flow ratesand longer columns for better separation, reduced costs, and, most crucially,easy evaporation of the SFC mobile phase eliminating the need for time-consuming solvent evaporation and disposal (which often exceeds the purchaseprice). The ability to provide purified compounds in salt-free form is also seenas a potentially important advantage since organic salts of trifluoroacetic acid(TFA), which is traditionally used in HPLC as a modifier, have recently beenassociated with high rates of degradation during storage [12], and cell-basedassay interference [13]. Since both analytical and purification capabilities are atleast comparable to HPLC [14], the only remaining advance still requiredbefore SFC is adopted as a method of choice for library purification is itscombination with mass spectrometry (MS) detection. Although custom SFC/MS systems have been reported [15,16], it still remains to be seen if thetechnology gains wider acceptance and becomes commercially available.

In most current workflows, compounds are generally purified and subjected tovigorous analysis before being incorporated into the screening collection. Themost widely accepted purity cutoff for this purpose is generally greater than85 percent. Inevitably, the requirement for robust synthetic protocols, purifica-tion, and thorough structural analysis has resulted in fewer compounds andlibraries being produced. This, however, is generally considered to be anacceptable limitation given the industry’s current focus on quality.

2.2.2.2. Compound Storage The quality of screening collections can beinfluenced by the initial chemical and physical quality of compound samplesas well as their rate of degradation under storage conditions. Traditionally, thehandling of newly synthesized compounds had been a manual, resource-intensive process that included local storage and ad hoc distribution acrossprojects. In the late 1990s it became apparent that compound management hadto change in a major way in order to be compatible with the requirements ofHTS. The increased number of screens, rapid expansion of corporate collec-tions, and growing focus on quality demanded new approaches to compoundhandling. Indeed, by the turn of the century most drug discovery organizationshad developed integrated, automated compound management systems. The keyfeatures of these systems include centralized solid and liquid sample storesenabling reliable production, storage, and rapid delivery to HTS of high quality


dimethyl sulfoxide (DMSO) solutions with defined concentration. The creationof liquid stores was not trivial, however, since the prolonged storage of organiccompounds in solution can lead to significant sample degradation at roomtemperature [17]. Many solvents were initially explored, including DMF,methanol, and ethanol, but DMSO was universally adopted as a solvent ofchoice since it solubilizes the highest proportion of test compounds, andis compatible with both cell-based (0.1–0.2 percent v/v) and cell-free assays(1–5 percent v/v).

There still remain some drawbacks associated with this choice, which aremainly related to the hydroscopic nature of DMSO. It can absorb up to10 percent water in as little as 5 h under normal laboratory conditions, whichcan have a significant effect on compound concentration, solubility, anddegradation [18]. Samples containing 5 percent water in DMSO are less stablethan dry DMSO, and thus humidity control is considered critical for main-taining the integrity of repository compounds [9]. Therefore, in order to reducesample degradation many pharmaceutical organizations store their screeningcollections as frozen, dry DMSO solutions at temperatures from 41C to aslow as �201C, and low relative humidity to maintain water content below0.5–1 percent. Consequently, samples have to be thawed to produce solutionsfor HTS and then frozen again for long-term storage.

This freeze/thaw cycling can have profound effects on the sample integrity dueto compound degradation or precipitation [17]. Indeed, the repeated freeze/thawcycles, together with water uptake during reformatting and poor initial quality,were cited as the most common reasons for compromised sample integritythroughout the industry [19]. To address some of these issues, new liquidmanagement concepts have been introduced, such as storing samples in individualsealed tubes as small DMSO aliquots for single use, eliminating the need forrepeated freeze/thaw cycles, and minimizing exposure to air and moisture.

Interestingly, some organizations had adopted a quite opposite strategy,where rather than trying to avoid water absorption, samples are stored as10 percent water/DMSO solutions [20]. This controlled addition of waterpotentially turns the liability into an advantage. The 10 percent water additionlowers the freezing point of DMSO (181C), allowing samples to be stored assolutions at 41C to slow down any potential degradation, while eliminating theneed for potentially damaging freeze/thaw cycles. In addition, the water uptakeof DMSO/water mixtures is much slower compared to anhydrous DMSO,therefore further reducing problems related to volume increase and diminishedsolubility during storage.

2.2.2.3. Chemical Filters (Structural Alerts) To take full advantage of HTS,the original strategy was to screen every available compound in a company’scollection. Consequently, synthetic intermediates, reagents, and even catalystswere incorporated into screening collections. Following the realization that notevery conceivable structure may be suitable for screening, various computa-tional filters were developed to ensure that compounds containing undesirable


structural features, termed structural alerts, are removed from existing com-pound collections and acquisition efforts.

Removal of suspected false positives is a critical aspect of enhancing thequality of the HTS process and output. One of the most common causes of falsepositives in the early days of HTS was the presence of chemically reactivespecies, such as alkylating and acylating reagents, which can react irreversiblywith proteins and consequently interfere with an assay readout [21,22]. Easilyidentifiable chemically reactive groups were among the first structural alertsused as computational filters to flag and remove undesirable compounds fromexisting screening collections and future acquisition activities. However, thereare a range of chemotypes that are more difficult to capture by simple in silicofilters that can also interfere with certain types of assays via other mechanismsand appear as false positives. For example, fluorescent and colored compoundsare well known to produce misleading readouts in assays based on fluorimetricor colorimetric detection [23]. Similarly, aggregate-forming compounds could‘‘light up’’ as false positives in biochemical assays [24]. However, the task ofidentifying these ‘‘problem compounds’’ on the basis of their structure is lessthan trivial; application of robust orthogonal assays at the hit confirmationstage is critical to weed out compound and assay-related artifacts, and this issueis discussed in more detail in Section 2.6.

Chemical filters are generally not limited to assay interfering functional-ities, but also include features that would make molecules unattractive as apotential starting point for hit optimization. Maximal numbers for somegroups or atoms are defined, for example, cutoffs for halogen atoms:W3chloro or bromo atoms or W6 fluorines. Similarly, highly complex molecules,as measured by number of asymmetric centers or fused rings, are alsoconsidered undesirable, as well as ‘‘bland’’ or undevelopable structures thatlack functionalities generally associated with specific protein binding. Anumber of molecular complexity descriptors have been reported in theliterature that could be used to filter out compounds from both of theseextremes [25]. In addition, well-documented toxicophores such as aromaticamine, hydrazine, and diazo groups are also considered as structural alertsand have been used as exclusion or deselection filters [26]. There is a ongoingdebate within the medicinal chemistry community about how strictly tox-icophore filters should be applied when designing screening libraries orpurchasing commercial compounds for corporate collections. On one sideof the argument, rather than limiting the chemical diversity coverage of thescreening collection and therefore the chances of finding a hit, the presence ofa toxicophore should be tolerated since a suitable bioisostere could be foundduring the hit optimization. On the other hand, considering both the vastnessof chemical space and the intense challenges of drug discovery, one may arguethat a risk management approach of selecting a nearest neighbor lackingstructural liabilities could be a more profitable strategy. This is particularlycompelling given that toxicity is one of the major causes of failure in clinicaldevelopment [27].


Most research organizations would now apply a specific set of structuralalerts derived from a consensus of opinions of an in-house panel of experiencedmedicinal chemists. In addition, ongoing analysis of HTS data is invaluable toidentify frequent hitters, which are otherwise difficult to predict [28], and thisinformation can be fed into the design of new alerts.

The list is reviewed on a regular basis (e.g., annually) to ensure newknowledge and structural alerts are captured. In addition to applying relevantknowledge derived from internal projects and the literature, visual inspection ofcompounds selected for purchase for the screening collection is an effective wayof identifying new structural alerts and preventing undesirables slippingthrough existing chemical filters. Some of the most widely applied structuralalerts are listed in Figure 2.1.

2.2.2.4. Physicochemical Properties Retrospective analysis of combinatorialchemistry and HTS output in the mid-1990s raised concern since the majorityof the hits identified were large lipophilic molecules that were often difficult tooptimize for in vivo activity. Addressing these concerns, the seminal publica-tion in 1997 of the ‘‘rule of five’’ (Ro5) physicochemical property guidelinesfor drug permeability by Lipinski et al. has changed focus of drug discovery[29]. Their ease of calculation and conceptual simplicity has made the Ro5the leading descriptor of ‘‘drug-likeness’’ adopted across the drug discoverycommunity.

Compounds must NOT have any of the following:

Reactive and chemically unstable groups, such as:Anhydride, acylhalide, sulphonyl halide, alkyl halides except alkyl fluoride, N–S,–N=C=O, –N=C=S, –N=C=N–, –C=S, N–halogen, S–halogen, R–SH, epoxide, azide, aziridine, thiirane, Michael acceptor, aldehydes, imine, 1,2-dicarbonyl.

Potential toxicophores: –N–NH2, –N=N–, ArNH2, ArNO2, >2 ArOH, >2 fused aryl rings

Structurally unattractive featuresAny element other than H, C, N, O, S, F, Cl, Br, ISugar-like frameworks (C–O)n, where n>3Crown ethersLinear chain with (CH2)n, where n>6 More than 6 F atoms, >3 Cl, Br, I atoms

Compounds must HAVE the following:

At least one N or O atomAt least one non-cyclic bond

Figure 2.1. Structural alerts: Groups and structural features considered undesirablefor compounds in a screening collection.


Compounds displaying drug-like properties are associated with acceptableaqueous solubility and adequate oral bioavailability, and consequently have abetter chance of progressing successfully through the drug discovery anddevelopment trajectory [30]. For example, it has been shown that the meanproperties of compounds in development converge at each stage toward themean properties of marketed oral drugs, indicating that clinical candidateswith properties close to marketed oral drugs are likely to be more successful,whereas compounds with higher lipophilicity tend to fail in development [31].The fact that the physicochemical property means of US Food and DrugAgency (FDA)-approved orally administered drugs have not significantlychanged over the past 20 years (i.e. mean MW=343 Da and mean clogP=2.3) indicate that these parameters are not incidental but have real physiolo-gical relevance [32].

The publication of the Ro5 not only changed the course of medicinalchemistry but also provided a powerful demonstration of the importance ofin silico and in vitro predictors for in vivo pharmacokinetic properties [33]. Forexample, in an analysis of other parameters important in controlling absorp-tion, Veber suggested that compounds with r10 rotatable bonds andPSAr140A, or r12 H-bond donors and acceptors, have high probability ofgood (W20 percent) oral bioavailability in the rat [34].

It should be emphasized that the above observations should be used more asguidance since different drug administration routes [32] and the nature of thetarget may demand different property profiles [35,36]. For example, a programaimed at modulating a monoamine transporter in the central nervous system(CNS) would require ligands with a different profile in terms of logP andnumber of hydrogen bond donors and acceptors [37], compared to a projectaimed at identifying antimicrobial agents active in the urinary tract. It istherefore appropriate to adopt the Ro5 as necessary, but compliance with thisis not sufficient to create a drug alone [38].

The emergence of the concept of drug-likeness enhanced awareness withinthe medicinal chemistry community regarding the importance of physicochem-ical parameters and the fact that selecting and optimizing leads is a multi-parameter problem. However, when considering the optimal physicochemicalprofile of screening libraries it should be borne in mind that the primary aim ofHTS is to produce tractable starting points for discovery programs, rather thanready-made drugs.

An analysis of the properties of lead-drug pairs found that in comparison todrugs, lead structures on average exhibit lower lipohilicity and lower molecularcomplexity, expressed as lower MW and lower number of rotatable bonds andrings [39]. Indeed, incorporating lipophilic groups is an effective and commonlyemployed medicinal chemistry strategy for improving in vitro potency, for whichreason the process of optimizing leads into drugs often result in more complexstructures. Application of this strategy would be quite difficult for leads withproperties already at the top end of drug-likeness, without compromising Ro5guidelines. This would be highly undesirable since high lipophilicity has been


associated with not only poor pharmacokinetic properties but also promiscuity,resulting in increased risks of pharmacologically based toxicity [40,41]. Con-sequently, it has been proposed that when designing screening libraries,emphasis should be placed on compounds with ‘‘lead-like’’ properties [38,39]:MW r 460 Da; clogP �4 to 4.2; number of rotatable bonds r10; number ofrings r4; hydrogen bond donors r5; and hydrogen bond acceptors r9.

A study of a probability model of interactions between ligands and receptorsof varying complexities further reinforced the importance of lead-like conceptswhen designing screening libraries [42]. It demonstrated that as the systembecomes more complex the chance of observing a useful interaction for arandomly chosen ligand fails dramatically. This suggests that screeninglibraries containing large complex molecules have lower chance of producinga hit. Even if a hit is found, options to optimize using approaches that increaseclogP/MW to improve potency would be limited for large structurally complexleads.

The lower limits of ‘‘the smaller the better’’ philosophy are determined bybiological assay capabilities. Starting points with lower MW are likely to havelower potency, which may fall below the sensitivity of HTS assay at thestandard 10 mM screening concentration. Screening such compounds at higherconcentrations (e.g., W500 mM) is severely hampered by issues related tosolubility, purity, and interference with assay readout. The lead-like concepthas been pushed even further in recent years in the development of a novelapproach, fragment-based lead discovery, centered around Ro3 compoundsand relying on biophysical methods for screening (see Chapter 4).

It has become clear over the last decade that the success of the HTSparadigm is not only dependent on increasing throughput, in terms ofquantities of compounds synthesized and screened, but also on quality of theirdesign. For more details on the impact of physicochemical properties on in vitroand in vivo ADME properties see Chapter 8.

2.3. SCREENING COLLECTION ENHANCEMENT STRATEGIES

The implementation of new inclusion criteria and the consequent cleanupactivities during the past decade have resulted in a significant depletion ofscreening collections across the industry. In some cases, in excess of half thecollection was deemed unsuitable for screening [43,11]. This provided a strongindustry-wide impetus for enhanced acquisition programs. In the currentenvironment, strict quality control measures have been incorporated into theprocess in order to ensure that the mistakes of earlier acquisition campaigns arenot repeated [43,20].

Most corporate collection enhancement programs are based around threecomplementary acquisition strategies: internal project compounds, librariesdesigned specifically for HTS and compounds acquired from commercial


suppliers (Fig. 2.2). The first and arguably most critical approach is to ensurethat all compounds synthesized within internal projects are swiftly incorpo-rated into the screening collection. It is widely recognized that corporatehistorical sets often deliver high hit rates and attractive starting points with afavorable intellectual property (IP) position. Historically, compound transferto the screening collection was a manual ad hoc activity occurring mostly afterproject termination, by which time many of the most interesting samples wouldbe lost or used up. This also meant that compounds would lie idle for severalyears in chemist’s drawers before becoming available for screening.

Today, many research organizations have established centralized samplemanagement systems, which deliver newly synthesized project compounds to aproject’s primary assay screen and to a centralized HTS store at approximatelythe same time.

2.3.1. Screening Libraries

The question of which compounds to make from the vast chemical universe andhow to design screening libraries in order to increase the chances of identifyingtractable hits has both fascinated and challenged the drug discovery communityever since the advent of HTS. As discussed above, the results of the earlyrandom approach to the design of screening libraries fell well short of theperformance originally anticipated from HTS, in terms of both the number and

ScreeningCollection

InternalProjects

ScreeningLibraries

CompoundAcquisition

HTS

Figure 2.2. Main sources of compounds for screening collection.

2.3. SCREENING COLLECTION ENHANCEMENT STRATEGIES 31

quality of starting points for optimization. This stimulated massive efforts overthe past decade across the industry and in academia to improve library designmethods. There are two distinct design approaches currently in use. Thediversity-based approach is chemistry-driven design with the ultimate objectiveto enhance the coverage of diversity space that a screening collection represents,whereas the focused-based approach also considers target or target familyknowledge to incorporate biological relevance into the library design.

2.3.1.1. Diversity Libraries Although diversity continues to be an importantaspect in the design of screening sets, diversity-based design methods haveevolved significantly from the early days of combinatorial chemistry whenemphasis was primarily on large libraries often built around structurallycomplex and unusual scaffolds (a common core structure present in allmembers of a library produced by a common synthetic route). Diversityconsiderations are now more concerned with covering chemical space, mainlyby smaller ‘‘smarter’’ libraries built around more scaffolds, rather than largerlibraries with fewer scaffolds. Accordingly, the emphasis of compound collec-tion diversity analysis has shifted in recent years from complete structures totheir scaffolds [44]. This highlighted the bias that exists in current screeningcollections, and turned attention toward filling the gaps in scaffold space [45].

The selection of scaffolds for diversity libraries is predominately chemistrydriven. Novel synthetic strategies and methods are used to access previouslyunexplored drug-like or lead-like chemotypes. The structural novelty is animportant criterion addressing not only the gaps in the screening collection butalso the patentability of HTS hits, which is increasingly considered criticalwithin the highly competitive drug discovery environment. Hence, the evalua-tion of proposed scaffolds normally begins with an assessment of their noveltyin the primary and patent literature, using for example SciFinder and Marpat.(Fig. 2.3). A number of parameters are used to assess the library structurecoverage, including dimensionality, complementarity to the corporate screen-ing collection and the number of available building blocks. Generally, librarieswith higher and more proportionate dimensionality are highly valued (e.g.,10 � 10 � 10), whereas 1D libraries are often considered undesirable.Similarly, multidimensional libraries with a large difference in number ofbuilding blocks available for the least and most diverse points (i.e., morethan twofold) are also less favored.

The synthetic feasibility of a scaffold and corresponding library compoundsare also an important part of the overall library assessment. Scaffolds shouldbe accessible on around the 50–100-g scale preferably by well-precedentedchemistry, whereas library steps should be sufficiently robust to allow use of awide range of building blocks. Libraries with anticipated lengthy developmentand production timelines (e.g., W8 months) are generally considered to beunfavorable. Scaffolds should also be relatively small, displaying physicochem-ical properties that would allow for majority of structures within enumeratedlibraries to meet lead-like or drug-like criteria. In addition to the most


commonly used and most easily calculated physicochemical cutoffs (e.g., MWr 450; clogP r 4), the cost of building blocks and in silico filters relating toADME and toxicity properties could also be employed to select the mostattractive structures from the virtual library.

Once all undesirables are filtered out, the minimum number of compoundsrequired to cover maximum possible diversity space for the library is deter-mined. For that purpose the prefiltered virtual set is clustered using 2D and/or3D descriptors and similarity cutoffs (e.g. Tanimoto distance o0.3). Thereagents are then selected for the best coverage across the clusters (i.e., W50percent), aiming for 10–15 compounds per cluster to provide a greater chance ofidentifying a hit [46].

The final selection could be further refined on the basis of experimental dataobtained from profiling a prototype set synthesized as a part of libraryevaluation or development process, in assays such as in vitro ADME, hERG,solubility, and Ames [43]. Particular emphasis is also placed on ensuringthat compounds are supplied in adequate quantity (e.g. W20 mg) and purity(e.g. W85 percent). Having access to solid sample is of considerable benefitwhen conducting hit confirmation activities as it enables rapid followup ofputative actives using freshly prepared DMSO stock solutions as well asallowing for a concomitant check of purity [109].

Scaffold selection

• Patentability/Novelty• Complementarity with scaffolds in collection• Dimensionality (2D or 3D)• Synthetic feasibility (scaffold and library)• Structural alerts (no reactive or unstable groups, and toxicophores)• Physicochemical properties (i.e., MW<300; c logP<3)• Biological rationale (for Focused libraries)

Building block selection

• Create virtual library (enumerate all available building blocks)• Physchem cutoffs (e.g., MW <450; c logP <4)• De-selection models: solubility, ADME, P450s, hERG)• Cost, availability and synthetic compatibility• Diversity (minimum number required for the diversity coverage)• Biological rationale (for Focused libraries)

Library production

• Consider testing prototype compounds in de-selection in vitro assays such as hERG, ADME, solubility• Sample quality criteria: e.g. >85% purity, >20 mg

Figure 2.3. Library design process.


2.3.1.2. Focused Libraries Given the vastness of chemical space, populatingbiologically irrelevant regions is one of the main shortcomings related to librarydesign based solely on diversity. This concern is addressed by ‘‘focusing’’libraries using a model or hypothesis derived from information available aroundthe biological target or target family of interest [47]. The emphasis of this designmethod is on identifying and populating biological space, particularly in theregions that overlap with the space defined by drug-like properties [48]. Theprocess of designing focused libraries is similar to that previously described fordiverse libraries, except that in addition to diversity and physicochemicalcriteria, biological data are also used at the point of selecting a scaffold andbuilding blocks. Depending on the available information, the design of focusedlibraries could be ligand- or structure-based, or a combination of the two.

2.3.1.2.1. Ligand-Based Approaches The availability of a vast amount ofbiological data, particularly since the genomic revolution, makes ligand-basedlibrary design applicable to almost every target or target family of therapeuticrelevance. Especially useful for target families with little biostructural informa-tion available, this approach has been extensively employed to design librariesbiased toward G-protein coupled receptors (GPCRs).

Typically, the process starts with gathering and mining ligand informationfor the relevant target or target family from the literature, and commercialand corporate databases. The aim is to identify frequently represented cores,or ‘‘privileged structures,’’ around which focused libraries can be built. Evansand coworkers introduced the concept of ‘‘privileged structures’’ to describecores featuring in ligands for more than one target [49]. Since the introductionof the concept in 1988, a range of privileged structures have been reported inthe literature (Fig. 2.4); some of them are common among ligands with cross-target family activities while others appear target-family-specific, so-calledtarget family privileged structures [50]. Bemis and Murcko carried out a graphanalysis of commercially available drug molecules in order to extractprivileged motifs to use as scaffolds for library design [51]. Interestingly,this study revealed that only 3 percent (32 in total) of structure frameworksaccount for 50 percent of all the drugs. These findings suggest the existence ofpreferred molecular scaffolds with an intrinsic tendency for biological activitythat could be exploited by appropriate modification of peripheral groups toidentify ligands for a variety of biological targets. Although the true existenceof target-family selective privileged substructures is still a matter of debate[52], many of these have been employed to design successful focused librarieswith reportedly high hit rates [53]. While there are no rigorous classificationcriteria, privileged structures are typically 1–3 ring systems with sufficientframework rigidity to prevent hydrophobic collapse and present appendinggroups in a well-defined direction required for target recognition [54].Although a privileged structure should constitute a significant proportionof the final compounds in a focused library, depending on the number of


N

N

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H N

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35

diversity points, its MW should not be greater than 300 Da and logP o 3in order to furnish molecules with desirable properties. In practical terms,any scaffold of interest should be synthetically accessible at around100g scale.

Once a scaffold is selected, the next opportunity to further focus librarydesign is at the building block selection stage. Similarly to privileged structureidentification, biologically active structures could be analyzed for mostfrequently occurring, or ‘‘privileged,’’ peripheral groups. For example, ananalysis reported in 1998 employed a computational approach, RECAP, tofragment structures of biologically active molecules on the basis of a numberof common retrosynthetic steps [55]. Frequency analysis of the resultingfragments allowed identification of synthetically relevant privileged motifs,which in turn enabled design of building blocks with preserved bindingrecognition elements and suitable for library synthesis. Since the buildingblocks had been derived from biologically active compounds there is alikelihood that their incorporation into novel molecules would providesome biological bias. Given the importance of IP, many organizations nowgenerate proprietary building blocks in conjunction with their screeninglibrary production efforts.

One of the factors limiting the scope of this approach is the inherentlyrestricted number of privileged structures, which raises issues related tointellectual property. Compound novelty is generally considered almost asan important criterion for library design as biological rationale. Conse-quently, there is a need to continuously identify novel scaffolds withbiological relevance. One potential approach relies on bioisosteric replace-ments, a proven method which can be used to modify known privilegedstructures. For example, in the kinase field a bioisosteric replacement of thephenyl ring of the known indazole scaffold yielded a thienopyrazole inhibitor,which at the time of its design was not known in the literature (Fig. 2.5) [54].In this respect, combining proprietary knowledge with relevant literatureinformation can be a particularly effective approach to the discovery of novel,biologically relevant scaffolds for general screening libraries.

HN

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Scaffold present in>200 kinase patents

Novelscaffold

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Novel Inhibitor(KDR IC50 23 µM)

Figure 2.5. Bioisosteric approach to the design of novel scaffold [54]


A similar approach to identifying novel scaffolds for focused librariesemploys ligand-based virtual screening methods in a process referred to as‘‘scaffold hopping.’’ Typically, 2D or 3D descriptors derived from knownligands of a given target or target family would be used to select or prioritizenovel chemotypes proposed by medicinal chemists (see Chapter 3). Whateverdescriptors are used for virtual screening, tight integration of computational,medicinal, and synthetic chemistry knowledge is critical. There is very little pointin evaluating synthetically inaccessible chemical structures. In order to supple-ment pipelines of carefully ‘‘handcrafted’’ chemistry proposals for evaluation,extensive databases of in silico generated novel, biologically relevant andsynthetically feasible scaffolds were developed [56,57]. Some computationalmethods rely on work by Dolle, who has annually published reviews of theliterature reported on combinatorial libraries since the 1990s [58], to generatesynthetically accessible structural motifs for scaffold hopping [59].

Novel HTS hits can also provide good starting points for the design ofgeneral screening libraries, in a so-called hit explosion approach. It is acommon observation that compounds active at one target are pre-disposed toshow activity at another target(s) within the same or even a distant genefamily. For that reason, historical corporate collections often produce higherhit rates than any other subset in a screening collection [60]. Hit explosion is aparticularly attractive strategy since it not only addresses enrichment of thescreening collection with novel and biologically relevant structures, but canalso support the project initiated around the original hits. In an early exampleof this approach a set of 3500 compounds were synthesized around avalidated hit, covering a diverse spectrum of structures from direct analogsto more loosely related drug-like chemotypes [61]. The hit optimizationproject benefited from over a hundred direct analogs providing initialstructure-activity relationship (SAR) around the chemotype, whereas thescreening of the full set of compounds led to the discovery of an attractivestarting point for another GPCR target, a novel and potent oxytocinantagonist (IC50 260 nM). Importantly, the compounds found to be potentoxytocin ligands proved to be selective over a range of other GPCRs.

The ability to extract information from large databases containing structureand activity data is an important aspect of the focused library design process.Some of the databases that are most frequently referred to in the literature inrelation to the design of focused libraries are listed below:

� World Drug Index (WDI): a comprehensive database of named drugs,both marketed and in clinical development (trade names, USANs andCAS numbers). The data is extracted from journals and conferencereports [62].

� Wombat: a database that provides information about the biologicalactivities of small molecules extracted from primary medicinal chemistryliterature [63].


� MDL Drug Data Report (MDDR): a database covering the patentliterature, journals, meetings, and congresses [64].

� PubChem: provides information on the biological activities of small molecules.It is a component of NIH’s Molecular Libraries Roadmap Initiative [65].

� StARlite (SARs in the literature): a large and highly curated SARdatabase recently made publicly available from Molecular Biology La-boratory’s European Bioinformatics Institute (EMBL-EBI) [66].

An area recently enjoying a resurgence of interest is the biologically relevantand diverse space mapped by natural products. Once considered a major sourceof novel therapies [67], interest in natural products declined sharply during the1990s, which is attributed to a number of factors, including difficulties withtheir isolation and synthesis, high expectations from combinatorial chemistry,and the introduction of drug- and lead-like concepts [68]. However, recentcomparison studies showed that while natural products do differ from drugs ina number of properties and occupy different regions of chemical space, over60 percent of those registered in the Dictionary of Natural Products do notviolate Ro5 properties [69]. Selected natural-product-derived cores haveincreasingly been considered as attractive scaffolds for the design of naturalproducts like screening libraries [70]. The role of natural products in moderndrug discovery is discussed in Chapter 7.

2.3.1.2.2. Chemogenomics Approaches to Library Design Chemogenomics isa rapidly developing discipline that is increasingly being employed in librarydesign, particularly for libraries focused toward targets or target families withlimited biostructural information [71]. The basic tenets of the chemogenomicapproach are: (1) compounds sharing some structural similarity may also sharetargets and (2) targets sharing similar ligands are likely to share similar bindingsites [72]. By relating ligand structure directly to the target sequence, thechemogenomics strategy essentially relies on a classification of receptorsaccording to their putative ligand-binding sites rather than overall sequencehomology [73,74].

One of the earliest applications of chemogenomics in the design of focusedlibraries was in the GPCR area, reported by Crossley and coworkers in2003 [75]. An extensive study of mutagenesis data, together with a rhodopsin-based 3D homology model, enabled them to identify around 40 amino acidresidues critical for ligand binding across the GPCR target family. Combina-tions of amino acid residues, termed themes, define microenvironments thatare responsible for the recognition of specific structural motifs presented byligands. The microenvironments and themes are schematically represented inthe form of a logical map (Fig. 2.6). Any given member of the GPCR family Acan contain several of the 28 themes identified to date. For example, theinteraction of dopamine with the D2 receptor is characterized by two themes,


one of which contains the conserved TM3 Asp responsible for the salt bridgeformed with the basic amine moiety, whereas the other interacts with theelectron-rich aromatic motif (Fig. 2.6). To design a focused library using thisapproach, a scaffold is selected or designed on the basis of its overlay with twoor three of the themes specific for the receptor or group of receptors of interest[76]. Furthermore, the presence or absence of each of the themes wasannotated for every receptor in subfamily A, including orphan members.This enabled the classification of the family A GPCRs based on receptor-specific fingerprints describing the presence or absence of each of the themes. Afingerprint representation also facilitates hierarchical similarity clustering andmapping of GPCR space, which could be used to score libraries directedtoward a particular group of receptors. Focused libraries designed by thismethod delivered high hit rates (1–13 percent) for both amine and peptideGPCRs [76].

The chemogenomics target classification by ligand-binding site rather than thewhole sequence is more meaningful to drug discovery, and is likely to continueevolving into an important approach to the design of screening libraries.

2.3.1.2.3. Structure-Based Approaches Biostructural information derivedfrom NMR or X-ray crystallographic analysis has been successfully used in

Ser

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position of residues on trans-membrane helixes (TMs) important for ligand binding, andthe themes they are forming (large circles). (b) Amino acids and Themes involved ininteraction with dopamine with D2 receptor (adapted from Crossley et al. [75]).


drug discovery for many years [77]. Initially, the focus was on refining singlecompounds at the lead optimization stage; however, more lately the scope ofstructure-based drug design (SBDD) has broadened to include much earlierstages of the drug discovery process, including the design of focused libraries.Reflecting the level of available biostructural information and growing ther-apeutic interest, a majority of SBDD-based libraries reported in the literatureare directed toward kinase targets. Most of these libraries are focused toward asingle target, fully exploiting the high resolution of biostructural information[78], although there are also reports of the same principles of SBDD beingapplied to the design of target family focused libraries [70]. Several approachesare generally employed to leverage biostructural information for library design.One of them is de novo design, which is discussed in Chapter 6.

Docking and scoring is another SBDD approach, which is very similar toligand-based library design, except that biostructural information is used toselect suitable compounds for synthesis from an enumerated virtual library.Typically, structures that pass property filters are sequentially docked into theligand-binding site and prioritized on the basis of their docking scores [79,80].For general docking and scoring methods and their applications for structure-based virtual screening see Chapter 3.

An interesting library design method that combines protein structure andligand information has been recently reported by Waldmann and colleagues[81]. Inspired by an observation that protein spatial structure is evolutionarilymore conserved than amino acid sequence, the authors clustered togetherCdc25A phosphatase, acetylcholinesterase (AchE) and 11b-hydroxysteroiddehydrogenase with very low sequence similarities (o10% identity), but high3D similarity of their ligand-binding domains. Screening a small library (147compounds) derived from naturally occurring Cdc25A inhibitors producedpotent and selective inhibitors of the other members of the cluster with a hitrate of 2–3 percent (see Chapter 7).

In another example, clustering functionally unrelated enzymes on the basisof the similarity of their substrate binding-sites topology resulted in thediscovery of a previously unknown cross-reactivity of COX-2 inhibitors withhuman carbonic anhydrase [82].

When biostructural information is unavailable, computational efforts tobuild a 3D homology model around an experimentally determined structure ofa related protein could be considered [83]. Repositories like The SWISS-MODEL, Protein Model Portal, and Modbase contain 3D models for anumber of proteins. However, as they are often generated by automatedmethods without human intervention, these models can be of relatively lowaccuracy. Application of refinement methods is required for a more accuratecharacterization of the binding site and determination of the exact side-chainconformation, since even minor errors can render the model unusable fordrug discovery applications [84–86]. The development and improvement ofhomology modeling methods and refinement tools is a highly active area ofresearch [87].


2.3.2. Compound Acquisition

Purchasing commercially available compounds is an important aspect of screen-ing collection enhancement. Since a robust IP position around HTS hits isconsidered increasingly important for the success of optimization projects, asizeable majority of compounds entering screening collections in major phar-maceutical companies originate from proprietary libraries. In order to keeplibrary production costs at manageable levels, the number of compounds perscaffold can be very high (e.g. W1000), limiting their diversity coverage.Therefore, acquisition of commercially available compounds from vendors is awidely adopted complementary strategy to maximize the coverage of structuraldiversity within corporate screening collections. These compounds are fre-quently around 10 fold cheaper than exclusive compounds. There are in excessof 10 million compounds currently available for purchase from various vendors,which presents an excellent opportunity to significantly enhance the diversity ofany corporate screening collection.

Since its initiation in the early 1990s, compound acquisition in most researchorganizations has evolved from an ad hoc activity into an elaborate, quality-driven selection process. Early compound acquisition efforts were drivenprimarily by structural diversity, strongly biased toward unprecedented che-motypes. However, one of the concerns raised by such an approach is thatmany of these diverse molecules could be covering chemical space that is in factnot biologically relevant. Over the years it became apparent that in addition todiversity, other constraints need to be applied in order to provide high qualityselections, including physicochemical properties and biological relevance [88].

The first step of the process is to combine catalogs from selected vendors intoone database (Fig. 2.7) [89]. When compiling the list of preferred vendors, theirtrack record is reviewed, with particular attention paid to the reliability in termsof providing samples in high purity, the agreed quantity (e.g., 20–50 mg), and inan expeditious manner. As discussed previously, having reasonable access toquantities of solid sample is of value at the hit confirmation stage. Anadditional factor to be considered is that there should be minimal overlapwith catalogs from other vendors.

The combined catalog is then checked for duplications and compounds notavailable in sufficient quantities. This is then followed up by application ofstandard exclusion filters, such as physicochemical parameters (e.g., MWo 450;clogP o 4) and structural alerts (e.g., chemically unstable or reactive groupsknown as toxicophores). Once the database is purged of ‘‘undesirables,’’diversity-based methods could be applied and a final set of structures that aremaximally dissimilar from the corporate compound collection is assembled.Robust clustering methods capable of handling large data sets could be used tofacilitate an assessment of the structural overlap and complementarities ofcompound acquisition selections with the current screening collection [90].

There is an ongoing debate within the drug discovery community about thedegree to which, if at all, compound acquisition should be driven by predictive


models that attempt to identify compounds active at targets of interest. Someregard randomly populating chemical space as a wasteful and conceptuallyunattractive approach, which disregards the enormous enhancements madeover the recent decades in our knowledge of drug discovery (Fig. 2.8). To matchbiological and chemical spaces [91] one could consider carrying out virtualscreening before applying diversity methods, in order to bias the selectiontoward biologically relevant space (focused diversity), as depicted in Figure 2.8.

Assemble database:Collate catalogs from preferred vendors

Remove structures on the basis ofredundancy and insufficient quantity

Exclusion filters:PhysChem cut-offs (i.e., MW<450; clogP<4)

Structural alerts (undesirable groups)

Selection methods:Ligand and/or structure-based models

Diversity-based selection

Visual inspection

Final SelectionElectronic order to vendors

Figure 2.7. Compound acquisition process outline.

(a) (b)

Figure 2.8. Matching chemical and biological space: (a) Random filling of the

chemical space vs. (b) focused diversity.


The current knowledge-based in silico models are sufficiently powerful toprovide a relevant focus for the selection, but are also imprecise enough toallow for exploration of the edges of biological space and serendipitousdiscoveries.

As a final step, the process could also include a visual inspection of theselection by experts, usually experienced medicinal or computational chemists.This is an important ‘‘reality check,’’ since an estimated one million newcompounds become commercially available every year, raising the risk ofnovel, yet undesirable chemotypes slipping through existing filters. This is alsoa means of ensuring corporate structural alerts and chemical filters areregularly updated. In order to limit the workload, the selection can be splitamong a number of experts for reviewing. To reduce inconsistency [92],consensus between two or more medicinal chemists could be used to identifycompounds for rejection. Lastly, it is advisable that the final selection allows foraround 10 percent attrition at the ordering stage, for example, due to sampleavailability.

2.3.3. Size Matters

It has been estimated that the total number of possible small organicmolecules is between 1040 and 10100 [93]. Clearly, even if it was desirable,no organization would ever be able to cover this chemical space. Collectingjust one molecule of each of these structures would result in a quantity ofmaterial that exceeds the mass of the universe many millions of times over.Although the space relevant to drug-like pharmacological compounds ismuch smaller, it is still too large to be significantly represented by a realcompound collection. The principal question is, therefore, how densely doesthis space need to be covered?

2.3.3.1. Size of Screening Collections Analyzing molecular diversity bymeasuring molecular complementarities against a fully enumerated set oftheoretical target surfaces estimated that a screening collection of 24,000,000compounds would be required to deliver hits with nanomolar potency for alltargets [94]. The number is likely to be even greater if the calculation takesinto account the need for multiple chemical series. However, a strategy basedsolely on potency could be fundamentally flawed, and screening aimed atidentifying attractive starting points with more balanced properties overall,rather than nanomolar hits, is generally considered to be a more effectiveapproach to drug discovery [95]. Another model that relates chemicalsimilarity to biological activity indicated that screening collections containing2–3 million compounds might be sufficient to deliver multiple starting points[96]. Screening collections in large pharmaceutical companies are alreadyapproaching if not surmounting these numbers. They may ultimately expand


further over time, since advances in screening and assay technologies willcontinue increasing throughput and reducing costs, not to mention consoli-dation within the industry. However, it is inevitable that at some point in thefuture, research organizations will shift the focus from rapid expansion tomaintaining the size of the screening collections, while still continuing withthe quality-enhancement efforts. Indeed, an analysis of relationships betweencollection size and the probability of finding HTS hits suggests that reachingthe point of diminishing returns is inevitable as the size of a collectionincreases [96]. When ultimately the size of corporate collections reach thispoint, minimizing redundancy and increasing the diversity of the set will benecessary in order to increase the success of screening campaigns.

2.3.3.2. Size of Screening Libraries One of the questions that needs to beanswered when designing a library is related to its size. What is the mostoptimal number of analogs around a given core structure?

It has been observed that when a hit is identified after screening a largelibrary, closely related analogs often do not emerge as hits. In other words, evenif a series is active, the hit rate within the ‘‘active series’’ tends to be low. Ananalysis of historical data from 18 HTS campaigns estimated the average hitrate within active series of around 4 percent [97]. This indicates a relatively highrisk of missing an active series if it is not represented by a sufficient number ofanalogs. A highly diverse library may result in missing many active series.Consequently, on the basis of probability arguments, it was suggested thatscaffolds are most optimally represented by 50–100 related analogs. Hence, interms of building a screening collection the emphasis should be given ideally toa larger number of smaller libraries, rather than a smaller number of largerlibraries. However, since pragmatic considerations have also to be taken intoaccount when designing screening libraries, it should be noted that libraryproduction cost is directly related to the size. The cost per compound dropsrapidly for the first few hundred analogs and increases only asymptotically forlibrary sizes larger than a few thousand. Interestingly, structural diversityappears to increase in a nonlinear fashion as the library size increases. Ananalysis of 12 libraries containing 500–6000 compounds showed that diversity,measured by the statistical variance of the information content described byDaylight fingerprints, increased most rapidly up to a size of around 1000compounds [59]. The variance for libraries over 2000 compounds continued toincrease, but at a significantly slower rate. Therefore, a library size of around2000 compounds was suggested as a ‘‘pragmatic conclusion for both thediversity and cost optimum.’’ Screening larger libraries may also expedite thehit confirmation process and enable early acquisition of SAR information. Arecently reported probability analysis of historical data derived from screeningfocused libraries recommended a library size ranging from 200 to 650compounds. Libraries of this size should be sufficient to provide minimumconfirmatory SAR information (2–5 hits from the same chemical series) [98].


2.4. SCREENING STRATEGIES

There are three distinct strategies that one could consider when planning ascreening campaign: full deck, focused, and sequential screening. The advan-tages and disadvantages of each approach are briefly discussed in this sectionwhich begins, however, with a more fundamental question that occupied thedrug discovery community in the early days of HTS: is it preferable to screenmixtures or singles?

2.4.1. Mixtures versus Single Compound Screening

In order to meet the demands of the HTS paradigm in the early 1990s, thestrategy adopted was to synthesize and test compounds as mixtures [99]. Thisconceptually attractive approach not only increased the throughput of bothsynthesis and testing, but also offered significant savings in biological reagents.Very quickly, however, it became apparent that the approach is plagued byseveral major drawbacks. For example, a high level of false positives is oftenobserved due to the potential for synergistic interactions between weakly activecomponents in the mixtures. In addition, mixtures often contain not onlyintended final products but also intermediates and reagents used in the synthesis,which further increases the potential for false positives. Indeed, it was frequentlyshown that single components obtained by deconvolution of a highly activemixture would show no activity under the same assay conditions [100]. Thecombination of high false positive rates and the requirement for an often verychallenging deconvolution process prompted most of the industry to movetoward the synthesis and screening of discrete compounds by the end of the 1990s.

The few organizations that still screen mixtures employ highly specializedtechnology platforms, such as a photocleavable linker and single-bead encodingstrategy, developed to facilitate deconvolution and circumvent some of theissues associated with mixtures [101]. MS-based methods, such as the automatedligand identification system (ALIS), are particularly well suited for screeningmixtures [102]. The ALIS system combines size-exclusion chromatography(SEC) and MS to screen ‘‘mass encoded’’ mixtures of compounds with differentmolecular masses. Compounds that remain associated with the target proteinafter a short incubation are separated by SEC, dissociated and identified by MS.This is a generic approach with respect to target class and allows identificationof ligands with Kd values from 10 mM to the subnanomolar range.

2.4.2. Full Deck Screening

In this approach all compounds in the screening collection are subjectedto HTS. It is a particularly attractive option for new targets for which verylittle information is available. This is arguably the most commonly adoptedscreening strategy [103,43,106], and prompted rapid development of uHTStechnologies in order to enable research organizations to cope with the

2.4. SCREENING STRATEGIES 45

ever-increasing size of screening collections. Though full HTS involves costlyand resource-demanding efforts, that is, protein/cell line production and robustassay development, this approach often delivers novelty and complete sur-prises, which within the highly competitive drug discovery environment isincreasingly attractive [43].

2.4.3. Focused Screening

Following this strategy, only a subset of the compound collection would bescreened. The selection method could be based on structural diversity, targetknowledge, or a combination of the two. As in the design of focused libraries,knowledge-based selection methods rely on ligand and/or biostructural infor-mation related to the target of interest. For example, the selection of a focusedsubset from the screening collection could be based on structural similarity tocompounds known to be active at the same or a homologous target, or apharmacophore model derived from a set of active ligands. If correspondingbiostructural information is available, docking programs could also be used inthe selection process. Focused screening is most commonly adopted when thereare bottlenecks in assay throughput or protein supply. In addition, thisapproach could provide more reliable data compared to a full HTS campaign.One potential disadvantage of this intellectually attractive and resource-efficientstrategy is its inherent reliance on currently available knowledge, which maypreclude discovery of novel structures and previously unknown binding pockets[103]. In order to address this concern, focused screening sets are oftensupplemented with diverse subsets of the complete compound collection.Reduced cost is another frequently quoted advantage of focused screening;however, it should be noted that the cost of screening reagents is highlydependent on the scale of purchase, and focused screening tends to besignificantly more expensive on per-well basis than HTS. Furthermore, a failureof focused screening to produce attractive starting point would normally call fora full HTS campaign, which would result in increased timelines and cost [43].

2.4.4. Sequential Screening

Sequential screening consists of either an initial diversity-based or focusedscreen, followed by one or more subsequent screens enriched with analogs ofthe hits. At the start of each iteration, computational techniques such asrecursive partitioning are used to model the biological activity in terms ofstructural features and this can then be used to select the next set of analogs. Ineach iteration, clusters of active compounds are further expanded until asufficient number of chemotypes are identified [104].

Similarly to focused screening, this approach could alleviate issues related toassay capacity, protein availability, screening costs, logistics, and timelines,potentially allowing the use of more ‘‘physiological’’ assays. However, the needfor cherry-picking, reformatting, and hit confirmation at each iteration can lead


to a prolonged time frame compared to HTS if the process is not streamlined.In a recent example of sequential screening at Johnson and Johnson, acompetitor compound was used as the starting point and a similarity-basedscreening was used to select compounds for the initial screen. Differentcomputational search methods were used at each step and the team movedaway from competitor’s chemotype to a more novel scaffold with each cycle. Intotal, only 79 out of the 1 million compounds in the corporate compoundcollection were screened. Interestingly, the most potent hit from this sequentialscreen was not picked up in a subsequent HTS campaign [105].

2.5. HTS ASSAYS AND EQUIPMENT

The success of any HTS campaign is just as dependent on the quality androbustness of the assay as on the quality of screening collection. Throughput,cost, and data quality are three intricately linked elements that need to beconsidered when assessing HTS assay performance.

HTS had its origin in natural product screening in the 1980s whenfermentation broths were replaced with DMSO solutions of synthetic com-pounds [103]. Since then screening throughput has been progressively enhancedby increasing plate density and automation. The introduction of 96-well platesand reduced assay volumes (50–100 mL) increased screening throughput from50 to 7200 compounds per week by end of 1980s [103]. Further miniaturization,together with advances in assay technologies, led to the introduction of the384-well-plate format, enabling screening capacities of 10–100,000 compoundsper day (HTS). More recently, 1536-well format provides throughput of over100,000 compounds per day (ultra-HTS) [106]. This trend continues, withseveral reports of biological assays carried out in 3456-well microplates with atotal assay volume of 1–2 mL per well, although, there are still significanttechnological hurdles that need to be addressed before this technology becomesroutine [107].

In recent years, there has been a shift away from HTS-binding assays thatrely upon the displacement of radioactive ligands to more environmentallyfriendly assays that use fluorescent dyes as the detection method and give afunctional readout. Since the modulation of many drug targets causes a changein intracellular calcium levels, the most widely used dyes are calcium sensitive,for example, fluo-3. The most commonly used instrument for measuring thesedyes is the Fluorescence imaging plate reader (FLIPR), and this has beenwidely applied to GPCR and ion channel targets [108]. In addition todiscriminating between agonists or antagonists, another advantage of func-tional assays compared to binding assays is the possibility of identifying ligandsthat bind to allosteric-binding sites. These types of ligands are of increasinginterest for modulating GPCR and kinase targets since they have the potentialfor increased selectivity compared to compounds that bind to the orthostericsite.

2.5. HTS ASSAYS AND EQUIPMENT 47

Radiolabel assays involve a separation of the test compound and the assayreagents from the assay reaction product, and this has the advantage that thesignal window is larger and the potential for compound interference is minimized.Homogenous assay technologies (e.g., fluorescence resonance energy transfer,FRET), where the protein–ligand interaction is detected directly from themixturewithout the need for a separation step, have significantly enhanced screeningthroughput. Such screens require fewer reagent addition and transfer steps, andhence homogenous assays are much easier to miniaturize and automate.

Similarly, cell-free (biochemical) assays are relatively straightforward andhighly amenable to ultra-HTS, whereas more complex cell-based assays tend toproduce more information but at the expense of the throughput. Cell-freeassays provide mechanistic information on ligand–receptor interaction, whichis very useful for SAR determination and optimization efforts, and have beenapplied to most therapeutically important target classes, including GPCRs,nuclear receptors, ion channels, kinases, proteases, and phosphatases. On theother hand, cell-based assays provide richer and physiologically more relevantinformation about test compounds, including functional effect, as well asevidence of membrane permeability for intracellular targets. Selection of themost appropriate assay technology for an HTS campaign depends ultimatelyon the biological target of interest and type of information required. Forinstance, a cell-based assay in 386-well-plate format may be appropriate for anHTS campaign for a GPCR agonist project, whereas a cell-free assay in 1536well plates may be more applicable to uHTS for a kinase inhibitor project.

Bender et al. compared the performance of different assay formats in HTS[109]. They found that some assay formats were more successful than others interms of the number of hits that were followed up, with LC-MS beingparticularly reliable, FLIPR assays around average, and reporter gene assays(RGAs) being least reliable. This correlates with how close the assay readout isto the target of interest with an LC-MS screen detecting direct modulation ofthe target, whereas a RGA assay measures luciferase activity downstream ofthe target and is thus prone to nonspecific hits. The preferred assay readout washighly dependent on the target family with FLIPR assays preferred overAlphaScreen or scintillation proximity assays (SPA) for GPCRs, whereas forkinases the latter were superior. They also found that different assays can givesignificantly different results for the same compound set, suggesting thatscreening using multiple assay formats can add value in terms of the diversityof hits obtained.

Over recent years specialized equipment has become available for the highthroughput electrophysiological screening of ion channel targets, such asIonWorks Quattro and PatchXpress [110]. Electrophysiological screening hasthe advantage over fluorescence-based methods that false positives are lesslikely and compounds that bind to particular states of the channel can bedetected. The price for this higher quality readout is that the throughput issignificantly lower, with 100,000 datapoints per year being typical for auto-mated electrophysiology compared to tens of millions for a FLIPR.


Increasingly sophisticated cell-based assays have been emerging over recentyears such as high content screening (HCS), which combines high resolu-tion fluorescence microscopy and powerful image-processing to producemultiparameter data for each test compound [111]. In contrast to classicalHTS assays, which are based on detection of the mean response of a wholecell population in a single well, HCS can distinguish the individual responseof many cells in a single well, which may differ with respect to the stage ofcell cycle, transfection or natural variability. As a result, HCS enablescompound profiling in mixed cell populations to obtain simultaneously notonly the primary target activity but additional information such as celltoxicity or compound-related artifacts. In addition, HCS can provide valu-able information on intracellular changes upon ligand binding such asreceptor internalization, second messenger generation, protein recruitment,or compartmentalization. Having evolved from a highly specialized compound-characterization technique, HCS is being increasingly employed at early stagesof drug discovery, and its application in HTS can be expected in the not toodistant future [112].

Assay miniaturization is driven not only by the need for higher throughput,but also the desire to reduce the cost of HTS. Reagents and consumables aremajor contributors to the overall cost of the screening process, together with therobotic screening systems that are a pivotal part of HTS. Typical reagent costsare driven by the biological test samples, such as target protein, antibodies, celllines, and substrates, and sometimes can be prohibitively high. However, itshould be noted that a reduction in volume does not correlate linearly with theamount of biological material used in the assay. In order to achieve sufficientsignal intensity and data quality, lower volume assay may require proportion-ally more reagents than a standard assay. Consequently, transfer of a 384-well-plate assay to 1536-well format, resulting in a fourfold volume reduction, maygive only two- to threefold cost reduction.

Quality is another important factor that needs to be considered whendeveloping or selecting an HTS assay for a given target. Two main parametersthat are considered are the dynamic range, which reflects the signal windowdefined by the positive and negative controls, and data variability. Both of theseparameters are taken into account by Zu, the most commonly used metric forassessing the quality of an HTS assay [113] both during its optimization andexecution:

Z0 ¼ 1� 3scþ þ 3sc�ð Þmcþ � mc�

sc+= standard deviation of the positive control

sc� = standard deviation of the negative control

mc+ – mc� = mean value for the positive and negative control

2.5. HTS ASSAYS AND EQUIPMENT 49

Assays with Zu values W0.5 are generally considered to have acceptablecharacteristics for HTS. This metric is also helpful for monitoring andmaintaining consistent standards over multiple screening days, and assessingthe statistical quality of a whole HTS campaign. However, it has been widelyaccepted over recent years that HTS assays should not only be optimized interms of the statistical quality represented by the Zu factor, but also with respectto assay sensitivity to maximize the chance of finding weak binders [106].

2.6. HIT CONFIRMATION AND ASSESSMENT

The overriding objective of the active-to-hit phase [43] is to identify multipletractable chemotypes that make attractive starting points for a subsequenthit-to-lead campaign. In the first instance there are two complementary facets:to remove undesirable or nongenuine hits from the progression pathway and tosuccessfully mine HTS data in order to extract all efficient ligands that couldpotentially form useful starting points for further optimization. In this section,we will expand on the methodologies used in both of these areas and providesome relevant examples of the application of these techniques.

2.6.1. Hit Confirmation Workflow

A typical workup of an HTS campaign is described below. Depending on therequirements of the individual program or target class, the workflow can betailored to include more specific filters or more stringent criteria (e.g., in thecase of a backup program where some deficiency in the lead such as patentscope needs to be addressed at the outset). The exact order of the process can bealtered to suit the needs of a particular program (e.g., in the case of a high hitrate or where selectivity is an issue, this filter would be placed near the top ofthe screening cascade and could be done in parallel with retesting). Addition-ally, the number of retesting rounds can be dependent on the exact number ofcompounds being progressed as well as budgetary constraints.

One final comment is that the idealized process illustrated in Figure 2.9assumes screening has been carried out on discrete compounds; in the case ofpooled or mixture screening an additional step of decoding or deconvolution isrequired to discern the identity of the single compounds.

2.6.2. Post Screen Heuristics

Increasingly, significant attention is being paid to ensuring the quality andvalidity of HTS data by adopting a more holistic approach to the analysis of thescreening campaign. Historically, one would monitor key parameters such as Zuand confirmation rate during the course of an HTS effort as a performanceindicator. However, a multilayered approach is now recommended [114] toidentify potential process artefacts that cannot be pinpointed using Zu dataalone. Using more powerful visualization techniques, systematic effects leading


to spatial or temporal trends can be quickly identified, enabling early removalof putative false positives from further progression. An example of such avisualization is shown in Figure 2.10, where a clear plate effect is evident inrows A and B.

In addition to manual inspection of the data with an appropriate visualizer,pattern recognition software such as Genedata [115] can be used to interrogateHTS data for such effects on an automated basis.

It should be borne in mind, however, that systematic effects can also have animpact on the screening campaign through a high number of false negatives,which can be more difficult to assess. Despite this there are a number ofcomputational techniques that could be used to rescue false negatives. Suchtechniques construct models to predict which compounds should be activebased on consideration of both the primary data and a range of descriptorsrelated to chemical structure. Recursive partitioning [116] and Bayes modeling[117] are among the methodologies reported to have been used in this context.

2.6.3. Retesting at Single Concentration

Having ascertained the quality (or otherwise) of the primary data, the nextstage of the active-to-hit process generally involves retesting to confirm activity

HTS campaign

• Assessment of screen performance and systematic effects• Retesting at single concentration from stock solution

Confirmed Hits

• Full profiling (HO checklist)• Peer review

Hit Optimisation

• Consider progressing several confirmed hits

• Expert analysis and selection• Specificity and selectivity testing• Dose response from stock solution• Sample QC• Data analysis and synthesis• Dose response from solid sample

Confirmed Actives

Figure 2.9. Representative post HTS to hit optimization workflow.

2.6. HIT CONFIRMATION AND ASSESSMENT 51

at a single concentration point, usually 10 mM. The precise number ofcompounds involved at this stage depends very much on the individual screen.However, with the advent of automated compound storage and handlingfacilities, selection of tens of thousands of compounds for this process isfeasible within a reasonable time frame.

2.6.4. Expert Analysis

Following appropriate biological assessment of the putative hits, the next stageof the active–to-hit process generally involves selection of a number ofcompounds for more detailed biological evaluation usually in the form of adose response measurement. Prior to inspection of the data by an experiencedmedicinal chemist, the data set is usually filtered to remove undesirablefunctional groups [118] (so-called structural alerts, e.g., acyl halides, imines,aldehydes, epoxides, etc.), which can give rise to electrophilic protein-reactivefalse positives (assuming these have not been removed prior to screening whenthe HTS set was assembled). At this stage, some physicochemical filters mayalso be applied as well as any project-specific filters.

Historically, the selection of compounds for further progression was drivenby a hard cutoff in terms of percentage effect (e.g., W80 percent inhibition). Inrecent years the quest for more efficient and tractable ligands, coupled with theavailability of high throughput visualization techniques, has mitigated a more

Scatter Plot Scatter Plot

Pla

te c

olum

n

Plate row

Pla

te r

ow

Effect

20

15

10

5

P O M K I G E C A

A

C

E

G

I

K

M

O

P

60 80 100 120 140 160 180 200

Figure 2.10. (a) Platemap showing edge effects. The size of the markers indicates thepercentage effect of a compound in a given well. (b) Percentage effect of a given

compound vs. row. Rows A and B exhibit a tendency toward higher activity.


holistic approach. The inherent issue with arbitrary activity-based selection isdepicted graphically in Figure 2.11. By selecting only on the basis of primaryactivity, there is a tendency to neglect more tractable hit series which, while theymay have lower potency, could represent more ‘‘ligand efficient’’ compounds interms of potency per nonhydrogen atom as represented by their binding orpercentage efficiency index [119,120]. The joint approach of taking into accountwhat statistically is considered active in a screen [121] and the use of ligandefficiency indices can be the most effective means of ensuring that HTS data ismined effectively. The main objective is not to progress the largest number ofcompounds possible, but instead to ensure progression of the largest number ofchemotypes.

An example from our own laboratories shows how striking the differencecan be in the selection of compounds based on potency alone. Applying a hardcutoff of 60 percent would progress compound 2 but not the more tractabletemplate represented by compound 1. However, when binding efficiency index(BEI) is used, the selection would be biased toward the more efficient binder(Fig. 2.12).

Although there is considerable computational assistance in both filtering thehit list (vide supra) and in generating cluster information to group-relatedcompounds, the most significant effort in selecting compounds for progressionis made by the project scientists. The individuals involved will utilize theirexperience to ensure that any chemotypes progressed are likely to be bothtractable and active as well as trying to represent the chemical diversity of thehit set. It is crucial, therefore, that the data is presented in a form that enablesstraightforward visualization. Packages such as Pipeline Pilot [122] can be usedto generate key molecular properties (e.g., MW, rotatable bonds, clogP, PSA),which can then be visualized using commercial software such as Spotfire.

Prim

ary

Act

ivity

Tractable chemotype

Cluster

Singletonhit

Intractableseries

Arbitraryhit cutoff

Statisticalhit cutoff

Tractable chemotype

Figure 2.11. Comparison of arbitrary vs. statistically driven cutoffs and implications

for hit series selection.


2.6.5. Hit Confirmation

Having prioritized the HTS actives as described in Section 2.6.4, the com-pounds of interest are then selected for further biological determination andconfirmation of structure. Depending on the number of compounds of interest,the project team may wish to generate dose response data using 10-mM stocksolutions. Given the well-known degradation of DMSO stock solutions overtime [17], QC data on the compounds such as LC-MS can be generatedconcomitantly to IC50 data and this information used in further prioritizationdownstream.

In parallel with the confirmation of activity from the DMSO stock solutions,it may be prudent to determine the specificity and/or selectivity of the putativehits before progressing to a more detailed biological evaluation such as doseresponse measurements. Selectivity measurements at this stage are generallyproject specific (e.g., a counterscreen against a related target), and acquisitionof this data may be postponed until further down the cascade (e.g., afteractivity is confirmed in a solid sample). Specificity may also be warranted at thisstage to remove compounds that interfere either with the assay or through someundesirable mechanism, particularly where the hit rate is high. A particularissue with fluorescence-based assays comes from compounds that autofluoresceor quench fluorescence, giving misleading results in the assay readout [21]. Suchcompounds can be detected by measuring autofluorescence in the assay bufferor in the case of cell-based systems using a target-null cell system.

As described in Section 2.2.2.3, a phenomenon that has recently receivedconsiderable attention in hit confirmation activities is that of ‘‘promiscuousinhibitors.’’ Upon further validation, such compounds generally show no SAR,poor selectivity, and often bind uncompetitively to the target of interest makingoptimization extremely difficult. This is a compound-specific source of inter-ference, which has been shown by light scattering and electron microscopyexperiments to be attributable to aggregation of the compounds in the assay

S

N

NHN

N

N

S

S

O

O

NH

OO

NHN

NH

NH

O

1%I = 56MW = 234BEI = 2.4

2%I = 62MW = 504BEI = 1.2

Figure 2.12. Comparison of percentage cutoffs vs. ligand-efficiency-based metrics.


buffer [24]. Frustratingly, the incidence of this mechanism is not restricted to‘‘ugly’’ or problem compounds, which could be identified through applicationof one’s chemical intuition. A number of known drugs such as clotrimazole anddelavirdine have also been shown to aggregate in solution [123].

Fortunately, using a number of simple counterscreens one can rapidly identifycompounds that exhibit such behavior. Addition of detergent [124] can be usedas one means of teasing out promiscuous inhibitors as can measurement ofaggregates through dynamic light scattering (DLS) [124]. Combining these twoapproaches can be particularly effective in weeding out potential aggregators.Other techniques reported to have been used to detect promiscuous inhibitorsinclude rescreening hits and assessing protein-concentration-dependent IC50

values, addition of serum proteins such as BSA, and looking for reductions inpotency [125] and surface plasmon resonance [126]. In silico methods using ascoring scheme based on a three-layered neural network, which could correctlyclassify 90 percent of compounds in a test set, provide the potential to prioritizecompounds from large data sets [127].

With IC50 data from the stock solution in hand, a second round of selectionusing the processes outlined above may be initiated in order to identifycompounds for confirmation of activity from a solid sample, either purifiedfrom available stocks or through dedicated resynthesis. Retesting from a solidor powder sample is vital, given the known precipitation of samples fromDMSO solution resulting in lower than expected sample concentrations beingtested [128]. One or two exemplars from each emerging chemotype cangenerally be selected for further characterization. This stage of the process isregarded as being particularly crucial, as all further activity in the hit-to-leadcampaign hinges upon having a validated hit with confirmed structure andpotency.

Figure 2.13 indicates how structure confirmation is crucial at the outset.An HTS campaign looking for positive allosteric modulators of mGluR2, a

O

O NH2

O

N O

O

3 4

Figure 2.13. Presumed and actual structures of mGluR2 positive allosteric modulators[129].


metabotropic glutamate receptor, led to a hit with a presumed structure 3

[129]. Analytical evaluation of the original sample showed only traces of 3 tobe present, and resynthesis gave no activity upon retesting. Purification ofthe original active batch and extensive characterization indicated that 4 wasthe correct structure, and this was confirmed by subsequent retesting of thepurified sample.

2.6.6. Hit Profiling

Having confirmed both the identity and the activity of the hits emergingfrom HTS, the project team will generally compile a checklist of additionaldata for each emerging series. This activity serves two purposes: to identifyany deficiencies in the compounds that require attention in the hit-to-leadphase and to enable a head-to-head comparison of individual hit series,thereby allowing prioritization where multiple hit series exist. Table 2.1summarizes the type of data that is typically gathered around a hit series[130].

Table 2.1. Hit confirmation check-list populated with data for a typical hit

Hit Confirmation Check-list Hit profile

Affinity/Potency (pKi/pIC50) 6.2Selectivity over closely related targets 5 fold

Acceptable parameters (Ro5) MW 263/cLogP 1.7Experimental logD 1.6

Solubility (mg/L) – minimum 10� IC50

for reliable assay data50

Ligand Efficiency (kcal mol�1 per non H atom) 0.44

– Human microsomes (ml/min/mg protein) 30– Rat hepatocytes (ml/ml/106 cells) 70

– Rat microsomes (ml/min/mg protein) 60Plasma stability, t 1

2 (min) 120Caco-2 permeability (nm/s) A-B 90/B-A 120

CYP inhibition (pKi/pIC50) 6.1 (3A4)Structural alerts – toxicophores,

reactive groups, DEREK, etc.

aromatic nitro group

hERG pKi (dofetilide binding) 5.1

Available SAR 5 analogues (pIC50 W6)Synthetic feasibility Good (literature

synthesis, o 5 steps)

Novelty (e.g. SciFinder & Marpat) Confirmed IP position

InvitroTox

Physico

chem

ical

Properties

Invitro

ADME


The activity of individual hit compounds is of course paramount todetermining the optimization strategy and is therefore a prime considerationin ranking hit compounds. Of equal interest is selectivity against relatedtargets.

The physicochemical characteristics of the hit compounds are anotherimportant parameter that may require optimization in the hit-to-lead phase.As well as the Ro5 parameters [29], project teams could also assess ligandefficiencies [119] and measured physicochemical properties such as logD andsolubility to enable prioritization of hit series. It may be worthwhile toassess chemical stability in a variety of different vehicles/pH ranges at thisstage.

Allied to the physicochemical properties of a particular hit, DMPK proper-ties may also be assessed at this stage in order to identify any deficiencies goingforward. In addition to the generation of in vitro data, and where capacitypermits, it may be prudent to generate some in vivo data to fully benchmark ahit series. Using techniques such as rapid in vivo oral screening in rats [131],levels of oral exposure of the compounds of interest can be determined. Beyondthis, full in vivo PK determination on hit compounds has also been reported[132]. This may also give an early indication as to the correlation between invitro and in vivo DMPK data.

In order to understand any potential toxicology associated with a compound,a number of spot-checks may be carried out from both an in silico and in vitroperspective. Determination of any putative toxicophores is possible usingsoftware packages such as DEREK [133], and measurement of off-target effectsleading to drug–drug interactions or cardiovascular safety concerns can beachieved through testing against CYP P450 enzymes and hERG, respectively.

There is generally a preference to work on a hit from a family of relatedcompounds compared to a singleton hit since an indication of whether anySAR exists in a hit series may have already been gleaned from the HTS output.However, due to the limitations of HTS data, it is certainly advisable to carryout substructure and/or similarity searching around hit compounds of interestto identify interesting analogs for further testing in more robust (e.g., doseresponse) assays even though such compounds were likely to have been part ofthe primary screen. The set of compounds for follow up may be augmented byextending the scope of the search to include the catalogs of commercial vendorsof screening compounds. It is certainly valuable to use a number of differentsimilarity searching algorithms in order to maximize the chances of findingrelated compounds with good activity, as previous studies have indicated thatthe compounds obtained can vary markedly depending on the search methodemployed [134].

Although chemical tractability forms a key consideration during the hittriage stage of analyzing HTS data, a more formal assessment is worthwhile atthis point. Key issues to note are the length of the synthesis, and if the templateis amenable to rapid generation of analogs since this can have a significantimpact on the time frame of the hit-to-lead phase.


Lastly, an assessment of the available IP space should be carried out at thisstage in order to determine the level of novelty and freedom to operateassociated with the chemotype. This is particularly important for hits originat-ing from commercial sources, which may have also been acquired by othercompetitor organizations.

Having sufficiently profiled the compounds of interest against the criterialisted above, some prioritization between different chemotypes should bepossible, thereby enabling allocation of resource to optmizing the hit serieswith the greatest likelihood of furnishing a lead series in an appropriate timeframe.

2.7. HTS: SUCCESSES, FAILURES, AND EXAMPLES

Evidence from the literature suggests that HTS does deliver hits that can beoptimized to clinical candidates. A survey of HTS labs in 2002 suggested that62 clinical candidates had emerged as a result of HTS efforts. The followingyear, that number had approached 74 and by 2005, 104 compounds wereclaimed to have been derived through an HTS campaign [135].

From the perspective of individual organizations, it is apparent that HTSis having an increasing impact on discovery programs. Data from a 6-yeartime frame at Bristol–Myers Squibb indicated that the proportion ofdiscovery programs initiated as a result of HTS increased from less than10 percent to in excess of 60 percent [7]. Data from GlaxoSmithKline alsomirrors this increase for the GPCR, enzyme and ion channel target classeswith HTS accounting for around 60–100 percent of lead compounds by2006, depending on the target class [136]. However, for certain targetfamilies, HTS has not proved successful, namely family B/C GPCRs andprotein kinases [3]. It is unrealistic to expect that HTS will deliver a lead forevery single target. Tractability issues aside, other hit-finding activities suchas focused/cross-screening, fragment screening, or literature-based ap-proaches should be considered not only for more challenging targets butalso as an adjunct to HTS.

Despite the limitations of HTS with respect to certain targets or targetclasses, it has proved successful in generating leads resulting in marketedcompounds. Table 2.2 shows some examples of recently marketed compoundsthat are directly attributable to HTS.

While the ultimate goal of any lead-finding effort is to bring a compoundto market, HTS has also proved invaluable for identifying tool compoundswhich have proved pivotal in the validation of new targets. In 1991, one ofthe first reports of HTS demonstrated the power of file screening bygenerating the first series of nonpeptide NK1 receptor antagonists typifiedby 11 in Figure 2.14 [140]. In this case, previous attempts at using de novodesign and molecular modeling to discover a nonpeptidic ligand were notsuccessful. Since then, HTS has proved to be a viable means of identifying


Table 2.2. Recent examples of marketed drugs and their progenitor compounds

identified through HTS efforts

Screening hit Optimized compound

S

O

O

NH

NHO

5

NH

NH

O

CF3

C

6, Sorafenib [137]

l O

N

NH

O

N NN

7

F

8, Maraviroc [138]

F

NH

O

N

N

NN

H2NN

NNH

O

NHS

CH3

OO

9

F

F

10, Sitagliptin [139]

F

N

ONH2

NN

N

CF3

N

O

NH

O

NH

O OF

NH

Cl

O

ON

N

NH O

Ph

11 12 13

Ph

Figure 2.14. Tool compounds derived from HTS efforts.

2.7. HTS: SUCCESSES, FAILURES, AND EXAMPLES 59

hits where no ligands exist or the current tools are suboptimal in somerespect. Examples include the identification of the first example of a brainpenetrant mGluR4 positive allosteric modulator 12 [141] and the first highaffinity Nav1.7 ligand 13, which provided a valuable screening tool to searchfor selective Nav1.7 blockers [142].

2.8. SUMMARY AND OUTLOOK

Since its beginnings in the early 1990s, lead discovery by HTS has evolved intoa mature scientific discipline. Early HTS strategies based on compoundnumbers alone have been firmly replaced by an emphasis on quality andscientific rationale. The introduction of drug-like and lead-like filters hasresulted in the retiral of many of the early combinatorial-chemistry-derivedscreening libraries. Over the past decade, ambitious collection enhancementprograms brought the size of screening files to over a million compounds inmany major pharmaceutical companies. Advances in automation, readouttechnologies, and assay miniaturization have enabled screening of thesemassive compound collections against a large number of biological targetswith ever-reducing turnaround times. The careful selection of compounds forscreening collections and a thorough validation of screening assays are nowconsidered as prerequisites for successful HTS campaigns.

The number of HTS-derived drugs has been steadily increasing in recentyears. Reports throughout the industry of enhanced quality and increasingnumbers of HTS-derived starting points for new discovery programs, andprogression into the clinic, are very encouraging. However, it is still prematureto assess the impact of improvements made over the last decade on the basis ofnew approvals due to the length of the clinical development process. HTS is stilla long way from being an optimal process, and continuous improvement isnecessary if it is to reach its full potential. Some of the challenges for the futureare highlighted below.

� Success rate: The success rate of HTS in terms of delivering tractablechemotypes for certain target classes and therapeutic approaches, forexample, GPCR family B/C and protein–protein interactions, is stilldisappointing. Further improvements in library design methods areneeded to enable a more systematic exploration of the relevant biologicalspace. The explosion of publicly available SAR data in recent years isexpected to drive rapid advances and major breakthroughs in this area.Similar attention will be given to further expansion of diversity spacerepresented by a screening collection, with particular focus on scaffolddiversity. This is likely to be addressed by smaller libraries designedaround novel and biologically relevant scaffolds accessed by novelsynthetic chemistry approaches.


� Data quality: Significant advances in assay technologies have been madeover recent years; however, none of these are free of artifacts derivedfrom the intrinsic physical nature of compounds in aqueous buffer. Thisnot only slows down the hit confirmation process, but also preventsextraction of more general information from HTS data that could beuseful in advancing our knowledge of ligand–protein interactions. Thisproblem could be potentially eliminated by the HCS paradigm. How-ever, major technological and scientific breakthroughs are requiredbefore such assays become amenable to an efficient HTS format. Theproblem of HTS artifacts could also be alleviated to some extent by therecently introduced concept of quantitative HTS (qHTS) [143]. Thisstrategy relies on generation of full dose response rather than singlecontraction data, producing rich data sets that can be directly minedfor reliable biological activities. A comparison with traditional single-concentration HTS revealed that qHTS can reduce both false positiveand false negative rates. Due to limited throughput, qHTS is moreapplicable to screening of focused sets (i.e., o100,000 compounds), orpotentially as a part of the hit conformation process. All of the abovedevelopments will inevitably lead to a significant increase in the numberof data points per well and consequently a significant increase in HTSdata output, which will require even more powerful data visualization,mining, and analysis software.

� Assay and screening technologies: Further miniaturization is required notonly to increase screening throughput, but also to reduce the escalatingcosts of HTS. This area has already seen remarkable advances with recentreports of biological assays carried out in 3456-well microplates. Theseadvances will have to be closely followed by the development of information-rich assays suitable for ultra high density screening formats. For example,many cell-based assays are still not transferable to 1536-well format.Another area that has over the years been generating a great interest in thefield of assay technologies is label-free detection, which could offerimproved assay sensitivity, greatly simplified assay procedures, and enableor facilitate assay development for traditionally difficult target classes.There are several different physical phenomena that are being investigatedfor this purpose, including magnetic and electric fields, optics, acousticwaves, thermal capacity, and heat transfer. Due to their relatively lowthroughput most of these technologies are currently confined to specialistassays that are used mainly for compound triaging and mode-of-actionstudies. However, the level of interest and activity in this field promisesrapid developments and greater impact on HTS in the not too distantfuture [144].

Despite the current limitations and the fact that the overall success ofthe HTS paradigm is still debatable, there is no doubt that this is the most

2.8. SUMMARY AND OUTLOOK 61

widely applicable lead discovery approach capable of providing a wide choiceof novel chemical entities for a broad range of biological targets. Conse-quently, HTS is likely to retain a central role in drug discovery and continueto provide a focus for major scientific and technological efforts for manyyears to come.

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Lead Generation Approaches in Drug Discovery (Rankovic/Lead Generation) || High Throughput Screening Approach to Lead Discovery